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Pradana, Kreshna Lucky
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Industrial Equipment Monitoring Dataset for Predictive Maintenance Analysis Pradana, Kreshna Lucky; Jefiza, Adlian
Technology Sciences Insights Journal Vol. 2 No. 2 (2025): Technology Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/t9mr4w17

Abstract

This study develops and evaluates a Support Vector Machine (SVM) model using a Radial Basis Function (RBF) kernel to detect faulty conditions in systems based on sensor data (temperature, pressure, vibration, humidity). The data is processed through normalization and split into training and testing sets. The evaluation results show an overall model accuracy of 0.93. The model is highly effective in identifying normal conditions (precision 0.93, recall 1.00), but less optimal in detecting faulty conditions (precision 0.96, recall 0.30), indicating a high number of false negatives and a low F1-score (0.45) for this class. The ROC AUC score of 0.892 indicates good overall discriminative ability. This performance gap is likely due to class imbalance. Enhancing faulty detection through class imbalance handling or further model optimization is recommended for critical applications.
Co-Authors Jefiza, Adlian